View-independent person identification fro human gait

نویسندگان

  • Zonghua Zhang
  • Nikolaus F Troje
چکیده

CA) Based on a three-dimensional (3D) linear model and the Bayesian rule, a method is e to identify human walkers from two-dimensional (2D) motion sequences taken from d viewpoints. Principal component analysis constructs the 3D linear model from a set of represented examples. The sets of coefficients derived from projecting 2D motion se onto the 3D model by means of a maximum a posterior estimate is used as a signatu walker. Simulating an identification experiment on a set of walking data we show th signatures show invariance across viewpoints and can be used for viewpoint-inde person identification. r 2005 Elsevier B.V. All rights reserved.

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تاریخ انتشار 2005